TheBojda

# MINST GAN Example
# based on https://www.tensorflow.org/tutorials/generative/dcgan

import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.keras import layers, models
import time

BUFFER_SIZE = 60000
BATCH_SIZE = 256

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import numpy as np
import tensorflow as tf
from tensorflow_core.python.keras import layers, models

text = "Hello World!"

input_text = text[:-1]
target_text = text[1:]
print(input_text)
print(target_text)

TheBojda

# Deep Q-learning CartPole example
# based on https://github.com/gsurma/cartpole.git & https://gym.openai.com/evaluations/eval_OeUSZwUcR2qSAqMmOE1UIw/

import gym
import random
import numpy as np
from os import path
from collections import deque
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

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import gym

env = gym.make("Breakout-v0")
observation = env.reset()
for _ in range(10000):
    env.render()
    action = env.action_space.sample()  # your agent here (this takes random actions)
    observation, reward, done, info = env.step(action)

    if done:

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# Word embedding tensorflow example
# based on: https://www.tensorflow.org/tutorials/text/word_embeddings

import io
import tensorflow as tf
from tensorflow_core.python.keras import layers, models, datasets
import tensorflow_datasets as tfds

(train_data, test_data), info = tfds.load(
    'imdb_reviews/subwords8k',

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import numpy as np
import tensorflow as tf
from tensorflow_core.python.keras import layers, models

words = ["cat", "dog", "apple", "orange", "car", "airplane", "man", "woman", "drink", "eat", "neural", "network",
         "tensor", "flow"]
dict_len = len(words)
word_index = dict((word, i) for i, word in enumerate(words))

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# Linear regression using GradientTape
# based on https://sanjayasubedi.com.np/deeplearning/tensorflow-2-linear-regression-from-scratch/

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

class Model:
    def __init__(self):
        self.W = tf.Variable(16.0)

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# Simple neural network with NumPy from 11 lines of code 
# source: https://iamtrask.github.io/2015/07/12/basic-python-network/

X = np.array([ [0,0,1],[0,1,1],[1,0,1],[1,1,1] ])
y = np.array([[0,1,1,0]]).T
syn0 = 2*np.random.random((3,4)) - 1
syn1 = 2*np.random.random((4,1)) - 1
for j in xrange(60000):
    l1 = 1/(1+np.exp(-(np.dot(X,syn0))))
    l2 = 1/(1+np.exp(-(np.dot(l1,syn1))))

TheBojda

# TensorFlow image classification example
# based on https://www.tensorflow.org/tutorials/keras/classification
# model generation: https://gist.github.com/TheBojda/f297544cc4864b2b10c2aad965339c58

from __future__ import absolute_import, division, print_function, unicode_literals

import tensorflow as tf

from tensorflow.keras import datasets, layers, models
from tensorflow.keras.models import load_model

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# TensorFlow CNN model training example 
# based on https://www.tensorflow.org/tutorials/images/cnn

from __future__ import absolute_import, division, print_function, unicode_literals

import tensorflow as tf

from tensorflow.keras import datasets, layers, models
import matplotlib.pyplot as plt